Fatty acid pattern analysis for predicting acute coronary syndrome

ABSTRACT

The present invention provides blood based methods for predicting risk of acute coronary syndrome in a subject.

CROSS REFERENCE

This applciation is a continuation application of U.S. patentapplication Ser. No. 12/421,784, filed Apr. 10, 2009, which claimspriority to U.S. Provisional Patent Application Ser. No. 61/135,822,filed Jul. 23, 2008, both of which are incorporated by reference hereinin their entirety.

BACKGROUND OF THE INVENTION

Predicting risk for acute coronary syndromes (ACS) remains an inexactscience. Although several recent risk prediction algorithms have beenproposed, the original and most widely used system is from theFramingham Heart Study. The Framingham Risk Score (FRS) was designed topredict the 10-year risk for major coronary events, and it does so witha c-statistic [area under the receiver operating characteristic (ROC)curve] of 0.7-0.8. All of these prediction algorithms generally includeage, sex, total (or low-density lipoprotein, LDL) cholesterol (C),high-density lipoprotein C (HDL-C), blood pressure and smoking anddiabetic status when assigning risk. Despite the utility of the FRS incoronary heart disease (CHD) risk prediction, there remains a need foradditional markers that improve upon this standard; while a number ofputative risk factors have been tested, few have added meaningfully tothe FRS.

SUMMARY OF THE INVENTION

In one aspect, the present invention provides methods for predicting therisk of acute coronary syndrome (ACS) in a human subject, comprising:

(a) measuring, in a fatty acid sample isolated from a blood componentfrom a human subject, an amount of:

-   -   (i) one or both of linoleic acid and gamma-linolenic acid; and    -   (ii) one or both of docosahexaenoic acid (DHA) and        eicosapentaenoic acid (EPA);

(b) comparing the amount of (a)(i) and (a)(ii) in the fatty acid sampleto an amount of (a)(i) and (a)(ii) in a control; and (c) predicting arisk of ACS based on the comparison.

In another aspect, the present invention provides methods for predictingthe risk of acute coronary syndrome (ACS) in a human subject,comprising:

(a) measuring, in a fatty acid sample isolated from a blood componentfrom a subject, an amount of:

-   -   (i) at least two of linoleic acid, gamma-linolenic acid, oleic        acid, palmitic acid, and alpha-linolenic acid; and    -   (ii) one or more of docosatetraenoic acid, docosahexaenoic acid        (DHA), trans-oleic acid, n-3 docosapentaenoic acid,        eicosapentaenoic acid (EPA), n-6 docosapentaenoic acid,        arachadonic acid, linoleic acid, gamma-linolenic acid, oleic        acid, stearic acid, and alpha-linolenic acid,

wherein there is no overlap between fatty acids in (a)(i) and (a)(ii)

(b) comparing the amount of (a)(i) and (a)(ii) in the fatty acid sampleto an amount of (a)(i) and (a)(ii) in a control; and

(c) predicting a risk of ACS based on the comparison.

Various preferred embodiments of each aspect are described in detailbelow. In one such embodiment, comparing the amount of the one or morefatty acids to a control comprises multiplying the percent total of therelevant fatty acids by a predetermined risk factor coefficient toproduce an individual fatty acid score and summing the individual fattyacid scores to produce a risk score.

In another preferred embodiment of each aspect, the method furthercomprises subjecting the percent total of the relevant fatty acids inthe fatty acid sample to an analysis selected from the group consistingof generalized models, multivariate analysis, and time-to-event survivalanalysis, to produce a modified risk score; wherein the modified riskscore is used to correlate with ACS in the human subject.

In a further preferred embodiment of each aspect, the method furthercomprises determining a Framingham risk score for the subject.

In a further aspect, the present invention provides physical computerreadable storage media comprising a set of instructions for causing adevice for measuring fatty acids in a fatty acid sample to carry out themethods of any of the aspects and embodiment of the invention.

BRIEF SUMMARY OF THE FIGURES

FIG. 1 depicts a flowchart describing recruitment of study subjects.Cases were drawn from a prospective registry of patients with aconfirmed diagnosis of ACS (2187). Patients that were excluded from thestudy (526) include those refused interview/access (128), discharged ortransferred prior to interview (308), deceased (27), and othercategories (63) including hard of hearing, too ill for interview,dementia, not English speaker and weekend admission. Among the total ofenrolled 1,661 patients, 1059 samples were obtained. No samples wereobtained for 602 patients, including those who refused extravenipuncture, had insufficient blood volume left over, were dischargedprior to interview, and refused to allow use of blood for biomarkertesting. Moreover, patients were also excluded (represented by “No data(100)”) due to incomplete information on some of the standard coronaryheart disease (CHD) risk factors including HDL(98), total cholesterol(89) and smoking status (2). Similarly, outpatient controls were alsoexcluded (represented by “No data (88)”) due to incomplete informationon some of the standard coronary heart disease (CHD) risk factorsincluding HDL(1), total cholesterol (1), hypertension (28), diabetesmellitus (58), age (1) and smoking status (19).

FIG. 2 is a graph demonstrating discrimination between ACS cases andcontrols in the validation set (n=450) with receiver operatingcharacteristic curves. Areas under the curves (c-statistics) werecompared for the modified Framingham risk score alone (c=0.77; brokengray line), the blood cell FA model alone (c=0.85; solid black line),and the FA plus modified Framingham model (c=0.88; dashed black line).C-statistics for both models including FAs were significantly greaterthan the Framingham model but were not different from each other (Table3; abbreviations as in Table 1). The diagonal line represents “Chance,”also known as “line of no-discrimination,” which is a random-guess line,showing that the percentage of being true or false is 50% for any pointson this line.

DETAILED DESCRIPTION OF THE INVENTION

In a first aspect, the present invention provides methods for predictingthe risk of ACS in a human subject, comprising:

(a) measuring, in a fatty acid sample isolated from a blood componentfrom a human subject, an amount of:

-   -   (i) one or both of linoleic acid and gamma-linolenic acid; and    -   (ii) one or both of docosahexaenoic acid (DHA) and        eicosapentaenoic acid (EPA);

b) comparing the amount of (a)(i) and (a)(ii) in the fatty acid sampleto an amount of (a)(i) and (a)(ii) in a control; and

(c) predicting a risk of ACS based on the comparison.

As disclosed in detail below, the inventors have discovered thatspecific fatty acid pattern in a fatty acid sample isolated from bloodcomponent can be used in the diagnosis and prognosis of ACS. Such fattyacid (FA) profiles are unexpectedly demonstrated to add substantially toACS case discrimination based on the state of art techniques such as theFramingham Risk Score. Even more surprisingly, the inventors havediscovered that certain FA profiles alone are able to discriminate ACScases from controls better than the Framingham Risk Score model. Thus,without knowledge of serum lipid levels, hypertensive and smokingstatus, personal/family histories of coronary heart disease (CHD), ordiabetes status, the methods of the present invention alone can be usedto predict ACS case status with a relatively high degree of accuracy,and thus may improve identification of patients at increased risk forACS, which can lead to earlier and more aggressive treatment.

As used herein, acute coronary syndrome (ACS) covers any group ofclinical symptoms compatible with acute myocardial ischemia (seeAmerican Heart Association web site,americanheartorg/presenter.jhtml?identifier=3010002). Acute myocardialischemia is chest pain due to insufficient blood supply to the heartmuscle that results from coronary artery disease, and includesmyocardial infarction and unstable angina.

ACS is usually associated with coronary thrombosis, but can also beassociated with cocaine use. Symptoms associated with ACS include, butare not limited to, chest pain, chest tightness, anemia, bradycardia,tachycardia, and heart palpitations. Thus, subjects presenting with oneor more of these symptoms will benefit from the methods of theinvention, as well as those subjects that have a family history of ACSor symptoms thereof, a genetic predisposition to ACS, other risk factorsfor ACS, and/or have previously suffered from ACS.

The methods of the invention can be used alone or in combination withother methods for diagnosing risk of and/or presence of ACS, includingbut not limited to electrocardiograms, blood tests for ACS biomarkers(including but not limited to creatine kinase MB, troponin I, troponinT, and natriuretic peptide), chest X-rays, and Framingham Risk Scoreanalysis, discussed in detail in the examples below.

As used herein, a “blood component” is whole blood or any specificfraction thereof. In various embodiments, the blood component isselected from the group consisting of red blood cells, whole blood,serum/plasma, platelets, white blood cells, and serum/plasma lipidclasses such as phospholipids, cholesteryl esters, triglycerides or freefatty acids. As noted below, blood component can be used for isolationof the fatty acid sample immediately upon obtaining the blood component,or it can be frozen and thawed prior to use. Fatty acids are isolatedfrom the blood component (to generate the “fatty acid sample”) usingstandard methods known to those of skill in the art, which compriseseither isolation immediately before carrying out the methods, orisolation and subsequent storage (on ice, frozen, etc.) of the fattyacid sample. In a preferred embodiment, the fatty acid sample isisolated from blood cell membranes, such as red blood cells, platelets,or white blood cells. In preferred embodiments, the fatty acid sampleincludes all of the fatty acids from the blood component, such as theentire fatty acid complement isolated from blood cell membranes.Exemplary methods for preparation of fatty acids from such bloodcomponents can be found in Block et al., Atherosclerosis 2008 April;197(2):821-8), and exemplary such methods are disclosed herein. As willbe understood by those of skill in the art, the fatty acid sample maycontain components other than fatty acids; the sample is “isolated” inthe sense that it is removed from its natural environment in the bloodcomponent, such as being removed from blood component membranes.

Determing an amount of the fatty acids can comprise any suitablemeasurement, including but not limited to determining an amount of afatty acid for the blood component as a weight percentage of total fattyacids, a molar percentage of total fatty acids, a concentration in theblood component, etc.

As used herein, a “control” is any means for normalizing the amount ofthe one or more fatty acids (FA) being measured from the human subjectto that of a standard. In one embodiment, the control comprisespre-defined fatty acid levels from a normal individual or population(ie: known not to be suffering from ACS), or from an individual orpopulation of subjects suffering ACS. In another embodiment, the controlcomprises a known amount of the one or more FA from the blood componentbeing sampled in ACS or non-ACS subjects. In these embodiments,predicting a risk of ACS in the subject based on the measuring maycomprise detecting a similar pattern of the fatty acid markers in thesubject as in a control subjects with ACS or in a control subject thatlater progressed to ACS, or may comprise detecting a dissimilar patternfrom control subjects known not to have had ACS.

In another embodiment, the comparison may comprise adjusting the amountof the fatty acids by an appropriate weighting coefficient, hereinafterreferred to as “a beta coefficient.” In one embodiment, the methodcomprises (a) multiplying the amount of the fatty acids (expressed as apercentage of total fatty acids in the sample) by a predetermined betacoefficient to produce an individual fatty acid score; and (b) summingthe individual fatty acid scores to produce a risk score. This riskscore can then be used in another equation to determine the probabilitythat a given subject has ACS or is at higher risk for developing ACSthan a person with a lower score. As will be understood by those ofskill in the art based on the teachings herein, beta coefficients can bedetermined by a variety of techniques and can vary widely. In oneexample of determining appropriate beta coefficients, multivariablelogistic regression (MLR) is performed using the fatty acids valuesfound within two groups of patients, for example, one with and onewithout ACS. There are several methods for variable (fatty acids)selection that can be used with MLR, whereby the fatty acids notselected are eliminated from the model and the beta coefficients foreach predictive fatty acid remaining in the model are determined. Thesebeta coefficients are then multiplied by the fatty acid content of thesample (expressed as a percentage of total fatty acids in the sample)and then summed to calculate a weighted score. The resulting score (“therisk score”) can then be compared with a particular cutoff score (ie: athreshold), above which a subject is diagnosed as at increased risk forsuffering from ACS or not at increased risk for suffering from ACS.

In various further embodiments, the fatty acid data (including, but notlimited to, percentage total of the individual one or more fatty acids,molar percentage of total fatty acids in the blood component, aconcentration in the blood component, etc.) are subjected to one or morealternative transformative analyses, including but not limited togeneralized models (e.g. logistic regression, generalized additivemodels), multivariate analysis (e.g. discriminant analysis, principalcomponents analysis, factor analysis), and time-to-event “survival”analysis to produce a modified score; wherein the modified score can beused to determine a risk for ACS.

In exemplary embodiments of the methods, Multivariable LogisticRegression (MLR) or Discriminant Analysis (DA) can be used asclassification methods for determining the probability of ACS. Fattyacids are assumed to be independent in MLR and to be inter-correlated inDA. The fatty acids are the predictor variables (X_(i)'s) and theprobability for ACS is the outcome (Y). The probability is a continuousvariable which can be dichotomized into two levels such as a binomial(O=Low risk or No Disease, 1=High Risk or Disease) response or severaldiscrete levels as an ordinal response (0, 1, 2, etc. for different risklevels for ACS).

The inventors have discovered that the amount of linoleic acid,gamma-linolenic acid, DHA, and EPA in isolated blood component fattyacid samples are each inversely correlated with the risk of ACS in thehuman subject. As used herein, “inversely correlated” means that ahigher amount of fatty acid relative to a control means the subject isat lower risk to have ACS, while “directly correlated” means a higheramount of the fatty acid relative to a control means the subject is morelikely to have ACS.

In this first aspect, any combination of (i) one or both of linoleicacid and gamma-linolenic acid; and (ii) one or both of docosahexaenoicacid (DHA) and eicosapentaenoic acid (EPA) can be used. In oneembodiment of this first aspect, the measuring comprises measuring anamount of linoleic acid and one or both of DHA and EPA in the fatty acidsample. In another embodiment, the measuring comprises measuring anamount of linoleic acid, DHA, and EPA in the fatty acid sample. In afurther embodiment, the measuring comprises measuring an amount ofgamma-linolenic acid and one or both of DHA and EPA in the fatty acidsample. In a still further embodiment, the measuring comprises measuringan amount of gamma-linolenic acid, DHA, and EPA in the fatty acidsample. As detailed in the examples that follow, the inventors havedemonstrated that the methods of each of these embodiments can be usedto diagnose ACS as a stand alone method, or in combination with othertechniques, such as Framingham Risk Score (FRS) analysis and/or use ofone or more of the FRS predictor variables (Sex, Age, Hypertension,Diabetes Mellitus, Current Smoker, Total-Chol, HDL-Chol) in a compositemodel with the fatty acids disclosed herein.

In another embodiment of this first aspect of the invention, the methodfurther comprises

(d) measuring an amount of one or more fatty acids selected from thegroup consisting of alpha-linolenic acid, palmitic acid, stearic acid,trans-oleic acid, n-6 docosapentaenoic acid, docosatetraenoic acid,arachidonic acid, oleic acid, n-3 docosapentaenoic acid, palmitoleicacid, trans-palmitoleic acid, and eicosadienoic acid in the fatty acidsample;

(e) comparing the amount of the one or more fatty acids selected fromthe group to an amount of the one or more fatty acids selected from thegroup in the control; and

(f) using the comparison in step (e) in predicting the risk of ACS.

These embodiments can be used to improve ACS risk prediction over thatprovided by the FA combinations recited above.

The inventors have discovered that the amount of alpha-linolenic acid,palmitic acid, stearic acid, n-6 docosapentaenoic acid, docosatetraenoicacid, arachidonic acid, oleic acid, n-3 docosapentaenoic acid,palmitoleic acid in an isolated blood component fatty acid sample areinversely correlated with the risk of ACS in a human subject, and theamount of trans oleic acid and eicosadienoic acid in the isolated fattyacid sample are directly correlated with the risk of ACS in the humansubject. This embodiment may comprise measuring the amount of 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11 or all 12 of the fatty acids recited in (d) incomparison to control.

In another preferred embodiment of the methods of this first aspect ofthe invention, the measuring comprises measuring an amount of linoleicacid, DHA, and EPA in the fatty acid sample, and further comprises

(d) measuring an amount of each fatty acid selected from the groupconsisting of stearic acid, alpha-linoleic acid, gamma-linolenic acid,palmitoleic acid, arachadonic acid, trans-palmitoleic acid,eicosadienoic acid, and trans-oleic acid in the fatty acid sample;

(e) comparing the amount of each fatty acid measured in step (d) to anamount of each fatty acid selected from the group in the control; and

(f) using the comparison in step (e) in predicting the risk of ACS.

This embodiment is detailed in Example 1 below; see, for example, Table2. This embodiment may comprise measuring the amount of 1, 2, 3, 4, 5,6, 7, or all 8 of the fatty acids recited in step (d) in comparison tocontrol.

In a further preferred embodiment of this first aspect of the invention,the measuring comprises measuring an amount of linoleic acid and one orboth of DHA and EPA in the fatty acid sample, and further comprises

(d) measuring an amount of one or more fatty acids selected from thegroup consisting of gamma-linolenic acid, alpha-linolenic acid, palmiticacid, stearic acid, trans-oleic acid, n-6 docosapentaenoic acid,docosatetraenoic acid, arachidonic acid, oleic acid, n-3docosapentaenoic acid, palmitoleic acid, trans palmitoleic, andeicosadienoic acid in the fatty acid sample;

(e) comparing the amount the one or more fatty acids measured in step(d) to an amount of the one or more fatty acids in the control; and

(f) using the comparison in step (e) in predicting the risk of ACS.

This embodiment is detailed in Example 2 below; for example, see Table4. This embodiment may comprise measuring the amount of 1, 2, 3, 4, 5,6, 7, 8, 9, 10, 11, 12 or all 13 of the fatty acids recited in step (d)in comparison to control.

In a further preferred embodiment of this first aspect of the invention,the measuring comprises measuring an amount of gamma-linolenic acid andone or both of DHA and EPA in the fatty acid sample, and the methodfurther comprises

(d) measuring an amount of one or more fatty acids selected from thegroup consisting of palmitic acid, alpha-linolenic acid, oleic acid andpalmitoleic acid in the fatty acid sample; and

(e) comparing the amount the one or more fatty acids measured in step(d) to an amount of the one or more fatty acids in the control; and

(f) using the comparison in step (e) in predicting the risk of ACS.

This embodiment is detailed in Example 2 below; for example, see Table4. This embodiment may comprise measuring the amount of 1, 2, 3, or all4 of the fatty acids recited in step (d) in comparison to control.

In a second aspect, the present invention provides methods forpredicting the risk of acute coronary syndrome (ACS) in a human subject,comprising:

(a) measuring, in a fatty acid sample isolated from a blood componentfrom a subject, an amount of:

-   -   (i) at least two of linoleic acid, gamma-linolenic acid, oleic        acid, palmitic acid, and alpha-linolenic acid; and    -   (ii) one or more of docosatetraenoic acid, trans-oleic acid, n-3        docosapentaenoic acid, docosahexaenoic acid (DHA),        eicosapentaenoic acid (EPA), n-6 docosapentaenoic acid,        arachadonic acid, linoleic acid, gamma-linolenic acid, oleic        acid, stearic acid, and alpha-linolenic acid,

wherein there is no overlap between fatty acids in (a)(i) and (a)(ii);

(b) comparing the amount of (a)(i) and (a)(ii) in the fatty acid sampleto an amount of (a)(i) and (a)(ii) in a control; and

(c) predicting a risk of ACS based on the comparison.

All definitions and embodiments disclosed in the first aspect of theinvention are equally applicable in this second embodiment, unless thecontext clearly dictates otherwise. This aspect of the inventionprovides further methods for diagnosing ACS, and numerous examples areprovided in Example 2 below (see, for example, see Table 4), and can beused as a stand alone method, or in combination with other techniques,such as Framingham Risk Score analysis. This embodiment may comprisemeasuring the amount of any combination of the (a)(i) and (a)(ii) fattyacids in comparison to control.

As recited in this aspect, the recitation that there is “no overlapbetween fatty acids in (a)(i) and (a)(ii)” means that the same fattyacid cannot satisfy the fatty acid requirement for both (a)(i) and(a)(ii). For example, if the (a)(i) fatty acids include linoleic acidand gamma-linolenic acid, then the (a)(ii) fatty acid must be somethingother than linoleic acid or gamma-linolenic acid.

In one preferred embodiment of this second aspect, the (a)(i) fattyacids comprise linoleic acid and gamma-linolenic acid, and the (a)(ii)fatty acids comprise one or more fatty acids selected from the groupconsisting of oleic acid, n-3 docosapentaenoic acid, stearic acid, andpalmitic acid. This embodiment may comprise measuring the amount of 1,2, 3, or all 4 of the (a)(ii) fatty acids in comparison to control.

In a further preferred embodiment of this second aspect, the (a)(i)fatty acids comprise gamma-linolenic acid and oleic acid, and the(a)(ii) fatty acids comprise one or more fatty acids selected from thegroup consisting of alpha-linolenic acid, docosatetraenoic acid, n-6docosapentaenoic acid, and arachidonic acid. This embodiment maycomprise measuring the amount of 1, 2, 3, or all 4 of the (a)(ii) fattyacids in comparison to control.

In another preferred embodiment of this second aspect, the (a)(i) fattyacids comprise palmitic acid and alpha-linolenic acid, and the (a)(ii)fatty acids comprise one or more fatty acids selected from the groupconsisting of trans-oleic acid, eicosapentaenoic acid (EPA),gamma-linolenic acid, docosatetraenoic acid, and linoleic acid. Thisembodiment may comprise measuring the amount of 1, 2, 3, 4 or all 5 ofthe (a)(ii) fatty acids in comparison to control.

In yet another preferred embodiment of this second aspect, the (a)(i)fatty acids comprise linoleic acid and alpha-linolenic acid, and the(a)(ii) fatty acids comprise one or more fatty acids selected from thegroup consisting of oleic acid and n-3 docosapentaenoic acid. Thisembodiment may comprise measuring the amount of 1 or both of the (a)(ii)fatty acids in comparison to control.

In a still further preferred embodiment of this second aspect, the(a)(i) fatty acids comprise palmitic acid and docosatetraenoic acid, andthe (a)(ii) fatty acids comprise gamma-linolenic acid.

In another preferred embodiment of this second aspect, the (a)(i) fattyacids comprise palmitic acid and trans-oleic acid, and the (a)(ii) fattyacids comprise linoleic acid.

Each of these preferred embodiments of the second aspect of theinvention are disclosed in more detail below in Example 2. Any of theseembodiments of the second aspect of the invention may further comprisemeasuring the amount of one or more further fatty acids to help furtherimprove statistical significance of FA marker performance in diagnosingACS. Such additional fatty acids may include any of those disclosedherein in either aspect of the invention that are not already recited inthe relevant embodiment.

In any embodiment of the first or second aspect of the invention, thefatty acid samples may be treated to produce fatty acid methyl esters(FAMES) prior to measuring the amount of the relevant fatty acids in thefatty acid sample. In one preferred embodiment, methylation comprisestreatment of the fatty acid sample with a boron-trifluoride solution,preferably at elevated temperature (90° C. or more; more preferablyapproximately 100° C.). The method may then further comprise extractingFAMEs from the tissue sample using a hexane solvent, and may furthercomprise separating the FAMEs by gas chromatography and determiningretention times of the FAMEs in the column by any suitable means, suchas use of a flame ionization detector (FID). Such retention times can becompared to a FAME standard and used in the FA measuring and comparingsteps recited herein. In one embodiment, the methods may comprisesubjecting the blood component to one or more freeze-thaw cycles priorto production of FAMES; this embodiment is of particular value when theblood component is frozen. Any suitable number of freeze-thaw cycles canbe used; it is within the level of skill in the art to determinespecific freeze-thaw conditions for a given analysis. Similarly, it iswithin the level of skill in the art, based on the teachings herein, todetermine suitable methylation conditions, gas chromatographyconditions, and FID conditions for a given assay. Exemplary conditionsare provided below.

In one embodiment, the measuring step comprises calculating a FAMEresponse factors for each FAME. Such calculations may be done by anysuitable method. In one embodiment, determining a FAME response factorcomprises:

-   -   (i) identifying the area counts for a reference fatty acid in        the FAME standard and dividing the area counts by the known area        percent of the reference fatty acid in the FAME standard; this        establishes a certain number of area counts per percent        composition in the FAME standard (hereafter the reference ratio)    -   (ii) multiplying the known percent composition of all other        fatty acids in the FAME standard by the reference ratio to        generate adjusted area counts    -   (iii) dividing the observed area counts for each fatty acid in        the FAME standard by the adjusted area counts to generate a        response factor;    -   (iv) multiplying the observed area counts for each fatty acid in        a sample by its own response factor to produce adjusted area        counts for each fatty acid in the sample; and    -   (v) summing all of the adjusted area counts for all of the fatty        acids in the sample and then dividing the adjusted area counts        for each individual fatty acid by the total adjusted area counts        in order to express each fatty acid in the sample as a percent        of total fatty acids in the sample.

In one example of this embodiment, assume that the area counts for areference FA in the FAME standard (such as C 16:0, palmitic acid) arefound to be 1000, and the known percent composition of palmitic acid inthe FAME standard is 10% of total fatty acids. The area counts aredivided by the percent composition (1000/10) to give a value of 100 areacounts per 1 percent of fatty acids. This value is then applied to allother fatty acids in the FAME standard. For example, if the knownpercent composition of oleic acid is 20% in the FAME standard then theexpected area counts for oleic acid should be 20×100, or 2000 areacounts. However, say the observed area counts for oleic acid are 1900counts. This is artificially low and may be adjusted upwards.Accordingly, the observed area counts are divided by the expected areacounts (1900/2000=0.95) to generate a response factor for oleic acid,here 0.95. This is done for all fatty acids in the FAME standard. Theresponse factors thus determined are then applied to the observed fattyacid area counts in the unknown samples. For example, assume that ahypothetical sample contained only 3 fatty acids (impossible in vivo,but illustrative). Assume that the area counts of oleic acid, linoleicacid and stearic acid in the unknown were 100, 200 and 400 counts,respectively, for a total of 700 area counts. Without adjustment forresponse factors, the percent composition would be 14%, 28% and 56%.Assume however, that the response factors (as determined from runningthe FAME standard in the same batch) were 0.90, 1.05 and 0.98,respectively.

Adjusting the observed area counts by the response factors would give90, 210, and 392 adjusted area counts. Summing these three =692 totaladjusted area counts. The percent compositions based on the adjustedarea counts then become 13%, 30% and 57%.

Note that any of the foregoing embodiments of any aspect may be combinedtogether to practice the claimed invention, unless the context clearlydictates otherwise.

In a further aspect, the present invention provides physical computerreadable storage media, for automatically carrying out the methods ofthe invention on a computer linked to a device for measuring fatty acidsin a fatty acid sample, such as those disclosed herein. As used hereinthe term “computer readable medium” includes magnetic disks, opticaldisks, organic memory, and any other volatile (e.g., Random AccessMemory (“RAM”)) or non-volatile (e.g., Read-Only Memory (“ROM”)) massstorage system readable by the CPU. The computer readable mediumincludes cooperating or interconnected computer readable medium, whichexist exclusively on the processing system or be distributed amongmultiple interconnected processing systems that may be local or remoteto the processing system.

EXAMPLE 1

We disclose a metabolomic approach based on blood cell fatty acid (FA)profiles in fatty acid samples isolated from blood cell components thatcan discriminate acute coronary syndrome (ACS) cases from controls, andhave discovered that analysis of such FA profiles can improvediscrimination of ACS compared with established risk factors.Multivariable logistic regression models based on FA profiles (MLR_(FA))and a modified Framingham risk score (MLR_(FR)s) were developed on arandom ⅔^(rd) training set and tested on a ⅓^(rd) validation set. Thearea under receiver operating characteristic (ROC) curves(c-statistics), misclassification rates, and model calibrations wereused to evaluate the individual and combined models. The MLR_(FA)discriminated cases from controls better than the MLRFRs (c=0.85 vs.0.77, p=0.003) and the FA profile added significantly to the FRS model(c=0.88 vs. 0.77, p<0.000I). Hosmer-Lemeshow calibration was poor forthe MLR_(FA) model alone (p=0.01), but acceptable for both the MLR_(FR)s(p=0.30) and combined models (p=0.22). Misclassification rates were 29%,23% and 20% for MLR_(FR)s, the MLR_(FA), and the combined models,respectively. FA profiles contribute significantly to the discriminationof ACS cases, especially when combined with the modified FRS.

Methods

Selection of Cases. Cases were drawn from a prospective registry ofpatients with a confirmed diagnosis of either acute myocardialinfarction²² or unstable angina²³ as previously described²⁴ and asoutlined in FIG. 1. All consecutive patients admitted to two hospitalsassociated with the University of Missouri-Kansas City School ofMedicine were prospectively screened for an ACS between March 2001 andJune 2004. Patients presenting with suggestive cardiac symptoms and/orischemic ECG changes, and a positive troponin blood test, wereconsidered to have had an MI. Patients were classified as havingunstable angina if they presented with a negative troponin test, newonset angina (<2 months) of at least Canadian Cardiovascular SocietyClassification class III, prolonged (>20 minutes) rest angina, recent(<2 months) worsening of angina, or angina that occurred within 2 weeksof a previous MI. Although EKG changes were not a requirement for adiagnosis of unstable angina, over two-thirds had ischemic EKG changeson admission. To further increase the accuracy of the unstable anginadiagnosis, those patients with a subsequent diagnostic study thatexcluded symptomatic ischemic heart disease (e.g. coronary angiography,nuclear or echocardiographic stress testing) or confirmed an alternativeexplanation for their presentation (e.g., esophagogastroduodenoscopy)were excluded. Three physicians reviewed the charts of all patients forwhom diagnostic uncertainty remained and attained consensus on the finaldiagnosis. With this approach, a total of 1,661 patients were includedin this registry and enrolled as described in FIG. 1.

Selection of Controls: Outpatient controls having blood drawn forroutine clinical testing were recruited from blood drawing centers atSaint Luke's Hospital (where 88% of the cases were derived) betweenMarch 2004 to March 2005 as outlined in FIG. 1. By selecting controlsfrom an outpatient clinical laboratory, we were able to include patientswith medical issues warranting care, but not being treated for an ACS.Patients entering the centers were passively invited (by a sign placedon the registration desk) to participate in the study by completing a2-page questionnaire and then allowing the phlebotomist to collect oneadditional 10 mL blood sample. Participation was limited to men andnon-pregnant women age 35 and older. From the pool of control samplescollected, cases were matched by age (5-yr windows), sex, and race(Caucasian vs. non-Caucasian for analysis) FIG. 1. A history of CHD wasnot an exclusion for either cases or controls, congruent with the intentof the study to identify patients at elevated risk for ACS, rather thanCAD. The study was approved by the Institutional Review Boards of bothhospitals.

Laboratory Methods: Blood cell FA composition was measured as previouslydescribed in detail¹⁰ during the same time period regardless of sampledate. As only frozen whole blood was available for this analysis, stepswere taken to isolate blood cell membranes from serum lipoproteins. Thiswas accomplished by placing 0.25 mL of thawed whole blood into a 1.75 mLof distilled water (to further assure erythrocyte lysis) andcentrifuging at 4° C. for 5 min. at 10,000 rpm in a TLA100.3 rotor in aTL100 tabletop ultracentrifuge (Beckman Instruments, Fullerton, Calif.).The supernatant containing plasma lipids and hemoglobin was decanted,the membrane pellet was resuspended in methanol containing 14% borontrifluoride and transferred to a screw cap test tube. This was heated at100° C. for 10 mm to generate FA methyl esters from the membraneglycerophospholipids. Equal portions of hexane and water were added toextract the methyl esters which were subsequently analyzed by flameionization gas chromatography (GC). GC analysis was carried out with anAgilent 6890 (Agilent Technologies, Palo Alto, Calif.) equipped with acapillary column (SP2560, 100 m., Supelco, Bellefonte, Pa.). Acustom-made mixture of FA (designed to mimic erythrocyte FA composition;GLC 673b, Nuchek Prep, Elysian, Minn.) was included as an externalstandard with each run for peak identification and for response factoradjustment. The response factor for palmitic acid was assumed to be 1.0,and that for all other FA was calculated based on this reference. FApeak areas were adjusted on a daily basis using these response factors,and reported as a percent of the total area for identified peaks. Eachpeak selected by the software algorithm was reviewed by a clinicalchemist (who had an M.S. degree, 20 years of clinical laboratoryexperience, and was blinded to case status). In situations where thesoftware identified peaks erroneously or failed to draw appropriatebaselines, these errors were manually corrected and the chromatogramre-integrated. Matched case-control sample pairs were always analyzed inthe same batch (20-30 samples), and were analyzed in random order alongwith standards and two controls (red blood cells high and low in omega-3FAs). About 50 samples initially produced unacceptable chromatograms.Upon re-analysis, all samples generated acceptable data. During the8-month analytic period, the Coefficient of Variation (CV) for highabundance FAs (>5.0%) was between 0.3% and 1.0%, and for low abundanceFAs (<1.5%) it was between 1.6% and 5.8%. The minimum detection level ofthe equipment was 0.01%.

For the cases, serum lipids were measured in the hospital laboratory byroutine enzymatic methods as clinically indicated within 1-2 days ofadmission. Lipid levels in control samples were determined in frozenplasma samples all at once at the end of the study using a Cobas Mira(Roche Diagnostics) in the Lipid Research Laboratory at Saint Luke'sHospital, a laboratory participating in the Lipid StandardizationProgram from the NHLBI/CDC. Appropriate corrections for differences inclinical lab and research lab lipid analyses were applied after apreliminary cross-lab comparison.

Statistical Methods: The original dataset contained 768 patientsdiagnosed with acute coronary syndrome (ACS) which were matchedone-to-one with controls on the basis of age, gender, and race. The1,576 patients were reduced to 1,348 due to incomplete information onsome of the standard coronary heart disease (CHD) risk factorsincluding: HDL, total cholesterol, self-reported hypertension (HTN),self-reported diabetes mellitus (DM), age, gender, and current smokingstatus shown in FIG. 1. If there were zeros present for any of the 18FAs then the smallest non-zero value was used as the minimum detectionlevel and the zeros were replaced by ½ of the minimum detection level.Two-thirds of the 1,348 subjects were randomly selected (without regardto matching or case status) as a training dataset for model building,while the remaining one-third was used later as a validation dataset toestimate prediction capabilities. The training and validation datasetscontained 445 and 223 cases, and 453 and 227 controls, respectively. Thestatistical models described below were analyzed by solely including theFAs, and additionally by adding the FAs to the 7 traditional CHD riskfactors comprising the FRS. Since the FRS is intended for riskprediction only in untreated subjects, we did a subgroup analysisincluding only those individuals who were not taking statin drugs.

Statistical Models

Modified Framingham Risk Score (FRS)—The FRS includes seven factors:age, sex, total (or LDL) C, HDL-C, systolic and diastolic bloodpressure, diabetes (based on blood glucose) and smoking status. Sincefor the controls, we did not have independent evidence of diabeticstatus or specific systolic and diastolic values, we includedself-reported diabetes and hypertension as dichotomous variables alongwith the other five factors in a multivariable logistic regression(MLR_(FR)s) model (the modified FRS). Natural log transformations wereused for HDL and total cholesterol to improve normality.

Fatty, Acid-Based Metabolomics—A model was developed (MLR_(FA)) usingstepwise selection to reduce the number of fatty acids to a subset ofthe most significant in collectively predicting case status; a p-valueof 0.01 was used to enter and remain in the model considering themultiple predictor variables. A third MLR model (MLRFRs+FA) wasdeveloped including the FAs selected in the MLR_(FA) combined with thetraditional CHD risk factors used in the MLR_(FR)s. Robust,non-parametric 95% confidence intervals (CI) of the parameter estimateswere obtained using bootstrapping method with 10,000 replicates from thetraining data set for both FA models. In addition to using the stepwiseselected FAs, four pre-specified FA metrics were also tested for theirability to add to the FRS model: the omega-3 index (EPA+DHA)²⁵, then-6:n-3 ratio²⁶, the total long-chain n-3 FAs (EPA+DHA+DPA), and theproportion of the long-chain n-3 plus n-6 FAs that were of the n-3family²⁷.

For each MLR model, a single continuous variable, a risk score, wascalculated (equation 1) as the linear combination of the parameterestimates (β_(i), i=0 to p) multiplied by each subject's FA levels(expressed as a percent of total FAs) or the traditional risk factorsfor the FRS (x_(ij)=1 to n) as follows:

riskscore=β₀+β₁ x _(1j)+β₂ x _(2j) . . . +βhd px _(pm)   Equation 1

The risk score was then used to determine (equation 2) the probabilityof case status, Pr(case). A Pr(case) >0.5 (the cut-point threshold) wasclassified as a case, otherwise as a control.

Pr(case)=1/(1+e ^((riskscore)))   Equation 2

Performance Metrics

Several metrics were examined to compare the performance of the variousmodels using the validation set.¹⁶⁻²⁰ Discrimination was assessed withthe c-statistic (concordance index) which summarizes the continuum ofmodel sensitivity and specificity values into a single measure. Positivelikelihood ratios combine in one number the sensitivity and specificityat the cut-point threshold by dividing the proportion of true positivesby the proportion of false positives. This statistic indicates howlikely it is that a case will have an abnormal test compared to acontrol, given 2 random patients, one of whom is a case and the other acontrol. Calibration was examined using the Hosmer-Lemeshow statistic, agoodness-of-fit measurement that compares predicted to observed countsof subjects by risk score deciles. Misclassification rates were alsodetermined.

The area under the ROC curve (c-statistic) was determined and thedifference compared to the MLR_(FR)s was tested. To account for multipletesting, the Dunnett adjustment was made to the 95% confidence intervals(CI). The standard error (SE) for the c-statistic was computed asdescribed by Hanley and McNeil²⁸ taking into account the fact that theareas were correlated since the same patient data were used in eachmethod²⁹.

Results

Case-control differences: Due to prospective matching on age, sex andrace, there were no differences in these attributes (Table 1). Asexpected, classic CHD risk factors were generally more common amongcases than controls. Twelve of the 18 FAs differed between groups, withcases having lower levels in 6 and higher levels in the other 6 FAs(Table 1).

Parameter estimates: Stepwise selection identified ten FAs significantlyrelated to ACS case status comprising the final mode! (Table 2).

Two FAs (eicosadienoic acid and trans oleic acid) were directly relatedto case status, whereas the other eight were inversely related. On aper-standard deviation basis, the greatest contributor to case statusprediction among the latter was linoleic acid, followed by stearic acid,DHA and the others.

Model Discrimination: Using the standard risk factors, and the parameterestimates for blood cell FAs, the ability of MLR models to discriminatecases from controls were compared, both alone and in combination (Table3 and FIG. 2).

The MLR_(FA) performed better than the MLR_(FR)s, with a c-statistic 8percentage points higher (p=0.003). Adding the FA profile to the FRSsignificantly increased the c-statistic of the latter by 11 percentagepoints (p<0.0001), whereas the FA-profile derived c-statistic was notsignificantly improved by including the standard risk factors (0.85 to0.88, p=0.16). Although the 10-FA profile added significantly to the FRSmodel, none of the simpler, pre-defined FA metrics (e.g., the omega-3index, the Lands' index, the n6:-3 ratio, etc.) added significantly toMLR _(FR)S discrimination (data not shown). As expected, in thestatin-naive subgroup, the MLR _(FR)S c-statistic was significantlyimproved over that in the group as a whole (0.81 vs 0.77, p⁼0.0002), butthe addition of the FA profile still added significantly to thec-statistic (0.89 vs 0.81, p=0.002) even in this subgroup.

TABLE 1 Baseline Characteristics of Cases and Controls (N = 1,348) CasesControls P- Variable (n = 668) (n = 680) value* Demographics Caucasian611 (91)† 624 (92) 0.84 Body mass index [kg/m²] 29 (25, 33)‡ 27 (25, 31)<0.0001 Myocardial infarction or revascularization (by history) 567 (85)141 (21) <0.0001 Family history of premature CHD 356 (53) 239 (36)<0.0001 Statin use 290 (43) 258 (38) 0.04 Standard CVD Risk Factors Age[yr] 59 (52, 70) 59 (52, 70) 0.94 Male 445 (67) 448 (66) 0.78Hypertension (by history) 423 (63) 361 (53) 0.0001 Total cholesterol[mg/dL] 176 (148, 206) 187 (159, 217) <0.0001 High density lipoproteincholesterol [mg/dL] 39 (32, 48) 48 (40, 57) <0.0001 Diabetes mellitus156 (23) 110 (16) 0.0009 Currently smoking 237 (35) 97 (14) <0.0001Fatty Acids (% total FA) saturated: Palmitic acid 22 (21, 24) 21 (20,23) <0.0001 Stearic acid 14 (13, 16) 15 (14, 15) 0.86 monounsaturated:Palmitoleic acid 1.4 (1.0, 1.9) 1.3 (1.0, 1.7) 0.21 Oleic Acid 18 (15,20) 17 (15, 19) 0.0006 trans unsaturated: trans Palmitoleic acid 0.42(0.30, 0.59) 0.33 (0.23, 0.50) <0.0001 trans Oleic acid 2.7 (2.2, 3.2)2.4 (1.9, 2.9) <0.0001 trans, trans linoleic acid 0.15 (0.11, 0.20) 0.15(0.11, 0.19) 0.06 n-6 polyunsaturated: Linoleic acid 14 (12, 16) 16 (15,18) <0.0001 γ-Linolenic acid 0.37 (0.32, 0.42) 0.43 (0.37, 0.49) <0.0001Eicosadienoic acid 0.25 (0.22, 0.28) 0.25 (0.22, 0.28) 0.85Eicosatrienoic acid 1.7 (1.5, 2.0) 1.7 (1.5, 1.9) 0.31 Arachidonic acid14 (12, 17) 14 (12, 15) 0.13 Docosapentaenoic acid 0.61 (0.46, 0.76)0.53 (0.41, 0.65) <0.0001 Docosatetraenoic acid 2.7 (2.1, 3.5) 2.5 (2.0,3.0) <0.0001 n-3 polyunsaturated: α-Linolenic acid 0.29 (0.21, 0.40)0.44 (0.31, 0.60) <0.0001 Eicosapentaenoic acid (EPA) 0.39 (0.30, 0.51)0.53 (0.38, 0.85) <0.0001 Docosapentaenoic acid 1.7 (1.3, 2.1) 1.8 (1.5,2.0) <0.0001 Docosahexaenoic acid (DHA) 2.6 (2.0, 3.6) 3.1 (2.4, 4.5)<0.0001 *Mann-Whitney (Wilcoxon rank-sum) nonparametric test was usedfor continuous variables, and Chi-square test was used for categoricalvariables. †n (%); ‡Median (Inter-quartile range).

TABLE 2 Odds ratios and estimated coefficients from the 10 fatty acidsincluded in the multivariable logistic regression models (per 1 SD; n =898) 1 SD Model without CHD risk factors Model with CHD risk factors (%of total Odds Est. Odds Est. Variable Structure FAs) Ratio 95% CI* (β)SE Ratio 95% CI* (β) SE Intercept — — — — 34.55 3.42 — — 7.29 2.67Linoleic acid (n-6) C18:2 2.79 0.15 0.10 to 0.21 −1.88 0.19 0.17 0.10 to0.24 −1.78 0.21 Stearic acid C18:0 1.72 0.22 0.15 to 0.30 −1.50 0.170.22 0.14 to 0.30 −1.52 0.18 Docosahexaenoic acid (n-3) C22:6 1.50 0.330.23 to 0.41 −1.12 0.13 0.37 0.26 to 0.48 −0.99 0.14 alpha Linoleic acid(n-3) C18:3 0.23 0.35 0.24 to 0.48 −1.04 0.16 0.32 0.21 to 0.44 −1.130.16 gamma Linolenic acid (n-6) C18:3 0.10 0.42 0.29 to 0.56 −0.87 0.130.46 0.31 to 0.62 −0.78 0.14 Palmitoleic acid C16:1 0.69 0.43 0.27 to0.63 −0.85 0.21 0.43 0.25 to 0.67 −0.85 0.24 Aracadonic acid (n-6) C20:43.12 0.43 0.30 to 0.58 −0.84 0.17 0.44 0.29 to 0.60 −0.83 0.18 transPalmitoleic acid trans C16:1 1.04 0.76 0.63 to 0.91 −0.27 0.10 0.76 0.62to 0.92 −0.27 0.10 Eicosadienoic acid (n-6) C20:2 0.06 1.37 1.12 to 1.730.31 0.11 1.43 1.15 to 1.85 0.36 0.11 trans Oleic acid trans C18:1 0.841.37 1.06 to 1.82 0.31 0.12 1.32 1.02 to 1.78 0.27 0.12 Male 0.92 0.56to 1.51 −0.09 0.23 Hypertension 1.17 0.76 to 1.84 0.16 0.21 DiabetesMellitus 0.79 0.46 to 1.31 −0.24 0.26 Current Smoker 2.86 1.79 to 5.071.05 0.26 Age (per 10 years) 1.10 0.91 to 1.33 0.10 0.09 TotalCholesterol† (per 1 SD ≈ 43 mg/dL) 0.95 0.75 to 1.19 −0.05 0.11 HDL†(per 1 SD ≈ 16 mg/dL) 0.56 0.43 to 0.71 −0.57 0.12 *95% confidenceintervals obtained using bootstrapping method with 10,000 replicates;†Natural log transformation was modeled.

TABLE 3 Diagnostic characteristics and misclassification error rates ofACS patients and controls # Hosmer- Positive Variables AUC LemeshowLikelihood Sensitivity Specificity Misclassification Rates (%) Model inModel c-statistic p-value Ratio (TP) (1-FP) Total Cases Controls AllSubjects (n = 450) (n = 223) (n = 227) FRS 7 0.77 0.30 2.5 0.70 0.72 2930 28 FA 10 0.85^(†) 0.01 3.2 0.79 0.75 23 22 25 FRS + FA 17 0.88^(‡)0.22 3.8 0.83 0.78 20 18 22 Statin Naïve Subgroup (n = 266) (n = 126) (n= 140) FRS 7 0.81^(‡) 0.15 2.9 0.73 0.75 26 27 25 FA 10 0.86 0.00 4.10.83 0.80 19 18 20 FRS + FA 17 0.89^(§) 0.01 4.6 0.85 0.81 15 18 12 FRS,Framingham Risk Score model; FA, fatty acid model; FRS + FA, combinedmodel. ^(†)P = 0.003 and ^(‡)P < 0.0002 when compared to FRS (allsubjects); ^(§)P = 0.002 when compared to FRS (statin naïve subgroup);TP, true positive; FP, false positive; AUC, area under the receiveroperating characteristic curve (c-statistic).

Model calibration: The only models for which calibration was acceptable(i.e., p>0.05) were those that included the FRS, either alone or whencombined with FAs (Table 3).

Model sensitivity and specificity: The positive likelihood ratio for theMLR_(FRS+FA) model was about 50% greater than that for the MLR_(FR)s(Table 3). Sensitivity and specificity were also higher with thecombined model, 0.83 and 0.78, respectively.

Model misclassification rate: The overall misclassification rate was 31%lower using the MLR_(FRS+FA) compared to the MLR_(FR)s model (Table 3).When restricted to cases, the MLR_(FRS+FA) misclassification rate was40% lower.

We herein demonstrated that the FRS has about the same ability todiscriminate ACS cases from controls cross-sectionally as it hasprospectively (i.e., the c-statistic is 0.7-0.8 in both cases),illustrating the robust character of this metric. More importantly,however, we have demonstrated that a metabolomic approach to ACS casediscrimination (whether based on FA profiles or any other smallmolecules) can significantly and substantially improve ACS casediscrimination over standard CHD risk factors. Indeed, blood cell FAprofiles were even more powerful than the classic risk factors(cholesterol, blood pressure, diabetes, smoking, etc.) in identifyingpatients with an ACS.

The relationship between risk for CHD and blood cell levels ofindividual FAs generally fit well with previous observations: inverseassociations with omega-3 and omega-6 FAs and direct associations withtrans FAs. However, the ACS discriminatory power of combinations of theFAs described herein is completely unexpected. Of the ten FAs includedin the model, increasing levels of eight were inversely associated withodds for ACS case status. These included the FAs of both omega-3 andomega-6 series, the monounsaturated FA palmitoleic acid, and thesaturated FA, stearic acid. Direct associations were found only withtrans-oleic (or elaidic) acid and eicosadienoic acid. Surprisingly, theFAs that had the greatest impact were the omega-6 FAs, notably linoleicacid, the most common dietary omega-6 FA. Many studies have reported anassociation between increased intakes and/or in vivo levels ofindustrially-produced trans FAs and CHD risk³⁰, whereas littleinformation exists for eicosadienoic acid. It is known to be anintermediate in a secondary biosynthetic pathway to arachidonic acidfrom linoleic acid³¹, and a potential substrate for cyclo-oxygenase³²but its physiological significance remains to be defined. Several n-6and n-3 FA-based metrics have been proposed as risk markers in CHDincluding the omega-3 index ³³, the n-6:n3 ratio ²⁶, and the Lands'index ²⁷.

However, in the context of an ACS event, none of these simple prior artFA metrics were able to add to the FRS. In contrast, we discovered thatanalysis of many blood cell membrane FAs can improve discrimination.Perhaps these other measures would have greater utility in predictingrisk for sudden cardiac death³⁴ than nonfatal CHD events.

One weakness of prior art case controlled studies was that the biomarkermeasured could be altered by the clinical event it is intended topredict, or having the event can alter behavior which, in turn, altersthe biomarker. But these concerns do not appear to apply to blood cellFA profiles of this invention obtained 1-2 days after the onset of ACS,since an MI does not appear to alter these profiles. Siscovick et al.reported that in a study with 18 primates,¹³ MI minimally altered redblood cell membrane long chain n-3 FA content, if anything tending toraise, not lower, it. We have confirmed these findings in a rat MI model(Shearer, et al., unpublished observations), and data from otherssupport the view that RBC FA patterns are not materially influenced byan MI.^(14,35,36) On these grounds, the FA profiles of this inventionhere very likely reflected pre-event status suggesting that they couldbe useful in prospective studies as well.

The discoveries of this invention can be used in the context ofdiagnosis. To do this, one can include standard diagnostic markers(e.g., creatine kinase MB, cardiac-specific troponin, ECG changes), inblood samples drawn upon admission. Thus, the potential of a FAmetabolomic approach for risk prediction, and the value of our findingscan be discerned after they have been tested prospectively.

Based on those criteria set forth by Vasan³⁷ that were addressable withthis study design (e.g. discrimination, positive likelihood ratios,misclassification rates, etc.), FA profiles performed well and showpromise as a new risk marker for CHD. Although the MLR_(FA) model failedcalibration, this was due to the presence of 4 subjects in the controlgroup with FA-based risk scores above the 90^(th) percentile, which washighly predictive of case status. It is possible, however, that thesecontrols could develop an ACS in the future and become cases. Othercriteria discussed by Vasan, such as the potential to reveal noveldisease mechanisms, also seem to be satisfied since FAs have been shownto impact a variety of pathological processes linked to CHD(inflammation, plaque instability, arrhythmic susceptibility,dyslipidemia, hypertension, etc.). Although we do not wish to be boundto any particular mechanism, these may be in part mediated byalterations in the activity of membrane-associated proteins.³⁸ Hence,pursuing membrane-mediated mechanisms of disease can lead to newinterventional strategies to reduce CHD risk. In addition, since FAprofiles can be altered by diet, and such alterations have been shown toreduce risk for CHD^(39,40), tracking FA profiles can lead to alteredclinical practice, another characteristic of a useful biomarker.

Advantages of this invention include a large sample size, arigorously-defined ACS population, detailed FA analysis, the use ofblood cell membrane FA patterns, and a comprehensive examination ofseveral metrics of model utility. Although this study was conducted in asingle metropolitan area and included few minorities, we believe thatthe approach is applicable to wider populations. The potential for biasexisted in the enrollment process as different methods were used tocollect demographic and health history data from cases and controls.Although the same questions were asked of both cases and controls thecase data were obtained by personal interview and chart review, whilecontrol data came from self-filled questionnaires. This couldtheoretically have led to inaccurate adjustment for covariates, but sucha problem would not have altered the relationships between ACS risk andblood cell FA content and would tend to bias our results to the null. Apotential exception to this assumption might be that ACS patients whodied before enrollment (whether in or out of the hospital), who haddementia or were too ill to interview could theoretically have had evenmore high-risk FA profiles than the ACS patients enrolled in the study.If so, this would limit the generalizability of the study to “healthier”ACS patients, and our findings would constitute a conservative estimateof the parameter effect sizes in the models tested here. Additionally,unlike cholesterol or blood pressure testing, inter-laboratorystandardization does not presently exist for blood cell FA composition(although efforts are currently underway to establish them). Hence, theFA percentages observed here may not be reproducible in other settingsusing other methods. Finally, we were not able to compare thediscriminatory power of FA profiles to that of other known (e.g.,current blood pressure, marital status, socio-economic class, exercise)or emerging (e.g., inflammatory markers)⁴¹ ⁴² CHD risk factors, some ofwhich could theoretically modulate or mediate the FA effect. Inconclusion, FA profiles added substantially to case discrimination basedon the FRS, but more surprisingly, FA profiles alone were able todiscriminate ACS cases from controls better than the Framingham model.Thus, without knowledge of serum lipid levels, hypertensive and smokingstatus, personal/family histories of CHD, or diabetes status, themethods of the present invention alone can be used to predict ACS casestatus with a relatively high degree of accuracy. These discoveriesindicate that the FA-based metabolomic approach to CHD risk assessmentof this invention has clinical utility.

EXAMPLE 2

Using similar methods to those described in Example 1 (except as notedbelow), we then compared the diagnostic value of combinations of 2 ormore fatty acids to the FRS in discriminating cases from controls. InTable 2, the 10-FA marker set has a c-stat=0.85 compared to FRSc-stat=0.77. These numbers considered 450 random subjects selected fromthe original 1348 patients, since the first 2/3 of the data was used totrain the parameter estimates, and then the remaining 1/3 was used totest the misclassification error rates. When all 1348 subject were usedto derive c-statistics, the FRS c-stat=0.749 (new Table 4), and thisc-stat is compared to the various combinations disclosed below, whichall provided improved diagnostic value over the FRS model, as noted inthe column labeled ‘Difference in c-statistic’, which means that thevalues are the differences in c-statistic compared to the FRS-derivedc-statistic.

TABLE 4 Use of fatty acid combinations to discriminate ACS patients fromcontrols (N = 1348) Area under Difference ROC curve Standard in(C-statistic) Error C-statistic Traditional Risk Factors Sex, Age,Hypertension, Diabetes 0.749 0.013 — Mellitus, Current Smoker, Total-Chol, HDL-Chol (ie: FRS) Fatty Acid Pairs Linoleic, Eicosapentaenoic(EPA) 0.812 0.012 0.063 Linoleic, Docosahexaenoic (DHA) 0.794 0.0120.045 Fatty Acid Triplets Linoleic, EPA, gamma-Linolenic 0.835 0.0110.086 Linoleic, DHA, gamma-Linolenic 0.834 0.011 0.085 Linoleic, Oleic,gamma-Linolenic 0.823 0.011 0.074 Linoleic, EPA, alpha-Linolenic 0.8230.011 0.074 (ALA) Linoleic, n-3 0.822 0.011 0.073 docosapentaenoic (n-3DPA), gamma-Linolenic Linoleic, EPA, Palmitic 0.822 0.011 0.073Linoleic, EPA, Stearic 0.822 0.011 0.073 Linoleic, EPA, Elaidic 0.8180.012 0.069 Oleic, ALA, gamma-Linolenic 0.818 0.012 0.069 Linoleic, EPA,n-6 0.818 0.012 0.069 docosapentaenoic (n-6 DPA) Oleic, Docosatetraenoic(DTA), 0.817 0.012 0.068 gamma-Linolenic Linoleic, EPA, DTA 0.817 0.0120.068 Elaidic, Palmitic, ALA 0.816 0.012 0.067 Linoleic, EPA,Arachidonic (AA) 0.816 0.012 0.067 Linoleic, EPA, Oleic 0.815 0.0120.066 Linoleic, DHA, ALA 0.813 0.012 0.064 Linoleic, DHA, EPA 0.8130.012 0.064 Linoleic, EPA, n-3 DPA 0.812 0.012 0.063 Palmitic, EPA, ALA0.810 0.012 0.061 EPA, Palmitic, gamma-Linolenic 0.809 0.012 0.060 EPA,ALA, gamma-Linolenic 0.807 0.012 0.058 EPA, Oleic, gamma-Linolenic 0.8070.012 0.058 DHA, ALA, gamma-Linolenic 0.806 0.012 0.057 Oleic, n-6 DPA,gamma-Linolenic 0.806 0.012 0.057 Linoleic, DHA, Elaidic 0.804 0.0120.055 Oleic, AA, gamma-Linolenic 0.804 0.012 0.055 Palmitic, DTA,gamma-Linolenic 0.802 0.012 0.053 ALA, Palmitic, gamma-Linolenic 0.8010.012 0.052 EPA, Palmitoleic, 0.801 0.012 0.052 gamma-LinolenicLinoleic, ALA, n-3 DPA 0.800 0.012 0.051 Linoleic, Stearic,gamma-Linolenic 0.800 0.012 0.051 Linoleic, Palmitic, 0.800 0.012 0.051gamma-Linolenic Linoleic, Stearic, DHA 0.800 0.012 0.051 Linoleic,Palmitic, DHA 0.800 0.012 0.051 Linoleic, Elaidic, Palmitic 0.800 0.0120.051 ALA, Palmitic, DTA 0.799 0.012 0.050 Linoleic, ALA, Palmitic 0.7980.012 0.049 Linoleic, DHA, Palmitoleic 0.796 0.012 0.047 Linoleic, DHA,cis-11,14- 0.796 0.012 0.047 Eicosadienoic Linoleic, DHA, n-3 DPA 0.7950.012 0.046 Linoleic, DHA, Oleic 0.795 0.012 0.046 Linoleic, DHA, AA0.795 0.012 0.046 Linoleic, ALA, Oleic 0.795 0.012 0.046 DHA, Oleic,gamma-Linolenic 0.794 0.012 0.045 *All p-values <=0.01.

REFERENCES

-   1. Assmann G, Cullen P, Schulte H. Simple scoring scheme for    calculating the risk of acute coronary events based on the 10-year    follow-up of the prospective cardiovascular Munster (PROCAM) study.    Circulation. 2002;105(3):310-315.-   2. De Backer G, Ambrosioni E, Borch-Johnsen K, Brotons C, Cifkova R,    Dallongeville J, Ebrahim S, Faergeman O, Graham I, Mancia G, Cats V    M, Orth-Gomer K, Perk J, Pyorala K, Rodicio J L, Sans S, Sansoy V,    Sechtem U, Silber S, Thomsen T, Wood D. European guidelines on    cardiovascular disease prevention in clinical practice. Third Joint    Task Force of European and other Societies on Cardiovascular Disease    Prevention in Clinical Practice (constituted by representatives of    eight societies and by invited experts). Atherosclerosis.    2004;173(2):381-391.-   3. Chambless L E, Folsom A R, Sharrett A R, Sorlie P, Couper D,    Szklo M, Nieto F J. Coronary heart disease risk prediction in the    Atherosclerosis Risk in Communities (ARIC) study. Journal of    clinical epidemiology. 2003;56(9):880-890.-   4. Executive Summary of The Third Report of The National Cholesterol    Education

Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment ofHigh Blood Cholesterol In Adults (Adult Treatment Panel III). Jama.2001;285(19):2486-2497.

-   5. Wilson P W, D'Agostino R B, Levy D, Belanger A M, Silbershatz H,    Kannel W B. Prediction of coronary heart disease using risk factor    categories. Circulation. 1998;97(18):1837-1847.-   6. Myerburg R J, Kessler K M, Castellanos A. Sudden cardiac death:    epidemiology, transient risk, and intervention assessment. Annals of    internal medicine. 1993;119(12):1187-1197.-   7. Grewal J, Anand S, Islam S, Lonn E. Prevalence and predictors of    subclinical atherosclerosis among asymptomatic “low risk”    individuals in a multiethnic population. Atherosclerosis.    2008;197(1):435-442.-   8. Lloyd-Jones D M, Liu K, Tian L, Greenland P. Narrative Review:    Assessment of C-Reactive Protein in Risk Prediction for    Cardiovascular Disease. Annals of internal medicine. 2006.-   9. Folsom A R, Chambless L E, Ballantyne C M, Coresh J, Heiss G, Wu    K K, Boerwinkle E, Mosley T H, Jr., Sorlie P, Diao G, Sharrett A R.    An assessment of incremental coronary risk prediction using    C-reactive protein and other novel risk markers: the atherosclerosis    risk in communities study. Archives of internal medicine.    2006;166(13):1368-1373.-   10. Block R C, Harris W S, Reid K J, Sands S A, Spertus J A. EPA and    DHA in blood cell membranes from acute coronary syndrome patients    and controls. Atherosclerosis. 2007.-   11. Harris W S, Poston W C, Haddock C K. Tissue n-3 and n-6 fatty    acids and risk for coronary heart disease events. Atherosclerosis.    2007;193(1):1-10.-   12. Albert C M, Campos H, Stampfer M J, Ridker P M, Manson J E,    Willett W C, Ma J. Blood levels of long-chain n-3 fatty acids and    the risk of sudden death. The New England journal of medicine.    2002;346(15):1113-1118.-   13. Siscovick D S, Raghunathan T E, King I, Weinmann S, Wicklund K    G, Albright J, Bovbjerg V, Arbogast P, Smith H, Kushi L H, et al.    Dietary intake and cell membrane levels of long-chain n-3    polyunsaturated fatty acids and the risk of primary cardiac arrest.    Jama. 1995;274(17):1363-1367.-   14. Kark J D, Manor O, Goldman S, Berry E M. Stability of red blood    cell membrane fatty acid composition after acute myocardial    infarction. Journal of clinical epidemiology. 1995;48(7):889-895.-   15. German J B, Gillies L A, Smilowitz J T, Zivkovic A M, Watkins    S M. Lipidomics and lipid profiling in metabolomics. Current opinion    in lipidology. 2007;18(1):66-71.-   16. Cook N R. Use and misuse of the receiver operating    characteristic curve in risk prediction. Circulation.    2007;115(7):928-935.-   17. Cook N R. Statistical evaluation of prognostic versus diagnostic    models: beyond the ROC curve. Clinical chemistry. 2008;54(1):17-23.-   18. Greenland P. Comments on ‘Evaluating the added predictive    ability of a new marker: From area under the ROC curve to    reclassification and beyond’ by M. J. Pencina, R. B. D'Agostino    Sr, R. B. D'Agostino Jr, R. S. Vasan, Statistics in Medicine (DOI:    10.1002/sim.2929). Statistics in medicine. 2008;27(2):188-190.-   19. Pencina M J, D'Agostino R B S, D'Agostino R B J, Vasan R S.    Evaluating the added predictive ability of a new marker: From area    under the ROC curve to reclassification and beyond. Statistics in    medicine. 2008;27(2):157-172.-   20. Pepe M S, Janes H, Longton G, Leisenring W, Newcomb P.    Limitations of the odds ratio in gauging the performance of a    diagnostic, prognostic, or screening marker. American journal of    epidemiology. 2004;159(9):882-890.-   21. Lemley K V. An introduction to biomarkers: applications to    chronic kidney disease. Pediatric nephrology (Berlin, Germany).    2007;22(11):1849-1859.-   22. Alpert J S, Thygesen K, Antman E, Bassand J P. Myocardial    infarction redefined—a consensus document of The Joint European    Society of Cardiology/American College of Cardiology Committee for    the redefinition of myocardial infarction. Journal of the American    College of Cardiology. 2000;36(3):959-969.-   23. Braunwald E, Antman E M, Beasley J W, Califf R M, Cheitlin M D,    Hochman J S, Jones R H, Kereiakes D, Kupersmith J, Levin T N, Pepine    C J, Schaeffer J W, Smith E E, 3rd, Steward D E, Theroux P, Gibbons    R J, Alpert J S, Faxon D P, Fuster V, Gregoratos G, Hiratzka L F,    Jacobs A K, Smith S C, Jr. ACC/AHA 2002 guideline update for the    management of patients with unstable angina and non-ST-segment    elevation myocardial infarction—summary article: a report of the    American College of Cardiology/American Heart Association task force    on practice guidelines (Committee on the Management of Patients With    Unstable Angina). Journal of the American College of Cardiology.    2002;40(7):1366-1374.-   24. Lanfear D E, Jones P G, Marsh S, Cresci S, McLeod H L, Spertus    J A. Beta2-adrenergic receptor genotype and survival among patients    receiving beta-blocker therapy after an acute coronary syndrome.    Jama. 2005;294(12):1526-1533.-   25. Harris W S. Omega-3 fatty acids and cardiovascular disease: a    case for omega-3 index as a new risk factor. Pharmacol Res.    2007;55(3):217-223.-   26. Harris W S. The omega-6/omega-3 ratio and cardiovascular disease    risk: uses and abuses. Current atherosclerosis reports.    2006;8(6):453-459.-   27. Lands W E. Long-term fat intake and biomarkers. The American    journal of clinical nutrition. 1995;61(3 Suppl):721S-725S.-   28. Hanley J A, McNeil B J. The meaning and use of the area under a    receiver operating characteristic (ROC) curve. Radiology.    1982;143(1):29-36.-   29. Hanley J A, McNeil B J. A method of comparing the areas under    receiver operating characteristic curves derived from the same    cases. Radiology. 1983;148(3):839-843.-   30. Willett W C. Trans fatty acids and cardiovascular    disease-epidemiological data. Atheroscler Suppl. 2006;7(2):5-8.-   31. Marcel Y L, Christiansen K, Holman R T. The preferred metabolic    pathway from linoleic acid to arachidonic acid in vitro. Biochimica    et biophysica acta. 1968;164(1):25-34.-   32. Koshkin V, Dunford H B. Reaction of prostaglandin endoperoxide    synthase with cis,cis-eicosa-11,14-dienoic acid. The Journal of    biological chemistry. 1998;273(11):6046-6049.-   33. Harris W S, Von Schacky C. The Omega-3 Index: a new risk factor    for death from coronary heart disease? Preventive medicine.    2004;39(1):212-220.-   34. Harris W S, von Schacky C. Omega-3 Fatty Acids, Acute Coronary    Syndrome, and Sudden Death. Current Cardiovascular Risk Reports.    2008;2:161-166.-   35. Jurand J, Oliver M F. Effects of acute myocardial infarction and    of noradrenaline infusion on fatty acid composition of serum lipids.    Atherosclerosis. 1970;11(1):157-170.-   36. Maidment C G, Jones S P, Lea E J. Changes in platelet membrane    fatty acids after myocardial infarction. Atherosclerosis.    1988;74(1-2):9-14.-   37. Vasan R S. Biomarkers of cardiovascular disease: molecular basis    and practical considerations. Circulation. 2006;113(19):2335-2362.-   38. Ma D W, Seo J, Switzer K C, Fan Y Y, McMurray D N, Lupton J R,    Chapkin R S. n-3 PUFA and membrane microdomains: a new frontier in    bioactive lipid research. The Journal of nutritional biochemistry.    2004;15(11):700-706.-   39. Wang C, Harris W S, Chung M, Lichtenstein A H, Balk E M,    Kupelnick B, Jordan H S, Lau J. n-3 Fatty acids from fish or    fish-oil supplements, but not alpha-linolenic acid, benefit    cardiovascular disease outcomes in primary- and secondary-prevention    studies: a systematic review. The American journal of clinical    nutrition. 2006;84(1):5-17.-   40. Marchioli R, Barzi F, Bomba E, Chieffo C, Di Gregorio D, Di    Mascio R, Franzosi M G, Geraci E, Levantesi G, Maggioni A P, Mantini    L, Marfisi R M, Mastrogiuseppe G, Mininni N, Nicolosi G L, Santini    M, Schweiger C, Tavazzi L, Tognoni G, Tucci C, Valagussa F. Early    protection against sudden death by n-3 polyunsaturated fatty acids    after myocardial infarction: time-course analysis of the results of    the Gruppo Italiano per lo Studio della Sopravvivenza nell'Infarto    Miocardico (GISSI)-Prevenzione. Circulation. 2002;105(16):1897-1903.-   41. Niu K, Hozawa A, Kuriyama S, Ohmori-Matsuda K, Shimazu T, Nakaya    N, Fujita K, Tsuji I, Nagatomi R. Dietary long-chain n-3 fatty acids    of marine origin and serum C-reactive protein concentrations are    associated in a population with a diet rich in marine products. The    American journal of clinical nutrition. 2006;84(1):223-229.-   42. Ferrucci L, Cherubini A, Bandinelli S, Bartali B, Corsi A,    Lauretani F, Martin A, Andres-Lacueva C, Senin U, Guralnik J M.    Relationship of plasma polyunsaturated fatty acids to circulating    inflammatory markers. The Journal of clinical endocrinology and    metabolism. 2006;91(2):439-446.

1-23. (canceled)
 24. A method for predicting the risk of acute coronarysyndrome (ACS) in a human subject, comprising: (a) measuring acombination of fatty acid markers in a fatty acid sample isolated from ablood component of a human subject, wherein the combination of the fattyacid markers, alone or together with the Framingham risk score (FRS)model, discriminates the ACS cases from controls better than the FRSmodel alone, the combination of the fatty acid markers comprising atleast the following fatty acids: linoleic acid, gamma-linolenic acid,and docosahexaenoic acid (DHA); (b) comparing the amount of the fattyacid markers measured in the fatty acid sample to a control; and (c)predicting a risk of ACS based on the comparing in step (b), wherein thedifference between the measured fatty acid markers and the controlexceeding a statistical significance indicates a risk of ACS.
 25. Themethod of claim 24, further comprising, prior to the measuring step:identifying the combination of the fatty acid markers from a bloodcomponent of a human subject, wherein the combination of the fatty acidmarkers discriminates the ACS cases from controls better than theFramingham risk score (FRS) model.
 26. The method of claim 24, whereinthe combination of the fatty acid markers comprises linoleic acid,gamma-linolenic acid, DHA, and eicosapentaenoic acid (EPA).
 27. Themethod of claim 24, wherein the combination of the fatty acid markersfurther comprises one or more fatty acid selected from the groupconsisting of stearic acid, alpha-linoleic acid, palmitoleic acid,arachidonic acid, trans-palmitoleic acid, eicosadienoic acid, andtrans-oleic acid.
 28. The method of claim 24, wherein the combination ofthe fatty acid markers comprises linoleic acid, gamma-linolenic acid,DHA, stearic acid, alpha-linoleic acid, palmitoleic acid, arachidonicacid, and trans-palmitoleic acid.
 29. The method of claim 24, whereinthe combination of the fatty acid markers comprises linoleic acid,gamma-linolenic acid, DHA, stearic acid, alpha-linoleic acid,palmitoleic acid, arachidonic acid, trans-palmitoleic acid,eicosadienoic acid, and trans-oleic acid.
 30. The method of claim 24,wherein the combination of the fatty acid markers further comprises oneor more fatty acid selected from the group consisting of palmitic acid,stearic acid, palmitoleic acid, oleic acid, trans palmitoleic, transoleic acid, trans, trans linoleic acid, eicosadienoic acid,eicosatrienoic acid, arachidonic acid, n-6 docosapentaenoic acid,docosatetraenoic acid, alpha-linolenic acid, eicosapentaenoic acid, andn-3 docosapentaenoic acid.
 31. The method of claim 24, wherein thecombination of the fatty acid markers comprises palmitic acid, stearicacid, palmitoleic acid, oleic acid, trans palmitoleic acid, trans oleicacid, trans, trans linoleic acid, linoleic acid, gamma-linolenic acid,eicosadienoic acid, eicosatrienoic acid, arachidonic acid, n-6docosapentaenoic acid, docosatetraenoic acid, alpha-linolenic acid,eicosapentaenoic acid, n-3 docosapentaenoic acid, eicosapentaenoic acid,and docosahexaenoic acid.
 32. The method of claim 24, wherein thecombination of the fatty acid markers comprises palmitic acid, oleicacid, trans palmitoleic acid, trans oleic acid, linoleic acid,gamma-linolenic acid, n-6 docosapentaenoic acid, docosatetraenoic acid,alpha-linolenic acid, eicosapentaenoic acid, n-3 docosapentaenoic acid,eicosapentaenoic acid, and docosahexaenoic acid.
 33. The method of claim24, wherein the measuring step comprises measuring a percent total ofeach of the fatty acid markers in the fatty acid sample as a percent oftotal fatty acids in the fatty acid sample.
 34. The method of claim 33,wherein the comparing step comprises: multiplying the percent total ofeach of the fatty acid markers in the fatty acid sample by apredetermined weighting coefficient to produce an individual fatty acidscore for each of the fatty acid markers; and summing the individualfatty acid scores to produce a risk score, wherein the risk score isused to correlate with ACS in the human subject.
 35. The method of claim33, further comprising subjecting the percent total of the fatty acidmarkers in the fatty acid sample to an analysis selected from the groupconsisting of generalized models, multivariate analysis, andtime-to-event survival analysis, to produce a risk score; wherein therisk score is used to correlate with ACS in the human subject.
 36. Themethod of claim 24, further comprising: determining a Framingham riskscore (FRS) for the subject based on one or more FRS risk factors; andincluding the ACS risk predicting step c) into the FRS model to modifythe Framingham risk score, wherein the modified Framingham risk score isused to correlate with ACS in the human subject.
 37. The method of claim24, wherein the blood component is selected from the group consisting ofred blood cells, whole blood, serum, platelets, white blood cells,plasma, cholesterol esters, triglycerides, free fatty acids, and plasmaphospholipids.
 38. The method of claim 24, wherein the blood componentis red blood cells.
 39. The method of claim 24, wherein the subject hasa family history of ACS, a genetic predisposition to ACS, and/or haspreviously suffered from ACS.
 40. The method of claim 24, wherein theacute coronary syndrome is myocardial infarction or unstable angina. 41.The method of claim 24, further comprising determining an ACS risk scorefor the subject based on one or more standard coronary heart disease(CHD) risk factors, wherein the CHD risk factors are selected from thegroup consisting of age, sex, total cholesterol (or LDL cholesterol),HDL-cholesterol, diabetes, smoking status and blood pressure; andincluding the ACS risk predicting step c) into the ACS risk score tomodify the ACS risk score, wherein the modified ACS risk score is usedto correlate with ACS in the human subject.
 42. A method for predictingthe risk of acute coronary syndrome (ACS) in a human subject,comprising: (a) measuring a combination of fatty acid markers in a fattyacid sample isolated from a blood component of a human subject, whereinthe combination of the fatty acid markers, alone or together with theFramingham risk score (FRS) model, discriminates the ACS cases fromcontrols better than the FRS model alone, the combination of the fattyacid markers comprising three or more of the following fatty acids:linoleic acid, gamma-linolenic acid, docosahexaenoic acid,eicosapentaenoic acid, oleic acid, alpha-linolenic acid, n-3docosapentaenoic acid, palmitic acid, stearic acid, elaidic acid, n-6docosapentaenoic acid, docosatetraenoic acid, palmitoleic acid,arachidonic acid, and cis-11,14-eicosadienoic acid; (b) comparing theamount of fatty acid markers measured in the fatty acid sample to acontrol; and (c) predicting a risk of ACS based on the comparing in step(b), wherein the difference between the measured fatty acid markers andthe control exceeding a statistical significance indicates a risk ofACS.
 43. The method of claim 42, wherein the combination of the fattyacid markers comprises at least five of the fatty acid markers.
 44. Themethod of claim 42, further comprising, prior to the measuring step:identifying the combination of the fatty acid markers from a bloodcomponent of a human subject, wherein the combination of the fatty acidmarkers discriminates the ACS cases from controls better than the FRSmodel.
 45. A method for predicting the risk of acute coronary syndrome(ACS) in a human subject, comprising: (a) measuring a combination offatty acid markers in a fatty acid sample isolated from a bloodcomponent of a human subject, wherein the combination of the fatty acidmarkers, alone or together with the Framingham risk score (FRS) model,discriminates the ACS cases from controls better than the FRS modelalone, the combination of the fatty acid markers comprising at least thefollowing fatty acids: linoleic acid, alpha-linolenic acid, arachidonicacid, eicosapentaenoic acid (EPA), and docosahexaenoic acid (DHA); (b)comparing the amount of fatty acid markers measured in the fatty acidsample to a control; and (c) predicting a risk of ACS based on thecomparing in step (b), wherein the difference between the measured fattyacid markers and the control exceeding a statistical significanceindicates a risk of ACS.
 46. The method of claim 45, further comprising,prior to the measuring step: identifying the combination of the fattyacid markers from a blood component of a human subject, wherein thecombination of the fatty acid markers discriminates the ACS cases fromcontrols better than the FRS model.